Skip to main content
SpringerLink
Log in

Evolutionary Machine Learning in Science and Engineering

  • Chapter
  • First Online:
Handbook of Evolutionary Machine Learning

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

  • 1470 Accesses

Abstract

Evolutionary machine learning (EML) has been increasingly applied to solving diverse science and engineering problems due to the global search, optimization, and multi-objective optimization capabilities of evolutionary algorithms and the strong modeling capability of complex functions and processes by machine learning (ML) and especially deep neural network models. They are widely used to solve modeling, prediction, control, and pattern detection problems. Especially EML algorithms are used for solving inverse design problems ranging from neural network architecture search, inverse materials design, control system design, and discovery of differential equations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abdel-Hamid, H., Notni, P.: Stellar population analysis from broad-band colours. Astron. Nach. 321(5–6), 307–314 (2000)

    Article  Google Scholar 

  2. Abouchekeir, S., Tchagang, A., Li, Y.: Adversarial deep evolutionary learning for drug design. In: 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp 01–09. IEEE (2021)

    Google Scholar 

  3. Adam-Bourdarios, C., Cowan, G., Germain, C., Guyon, I., Kégl, B., Rousseau, D.: The higgs boson machine learning challenge. In: NIPS 2014 Workshop on High-Energy Physics and Machine Learning, pp. 19–55. PMLR (2015)

    Google Scholar 

  4. Al-Yaseen, W.L., Idrees, A.K., Almasoudy, F.H.: Wrapper feature selection method based differential evolution and extreme learning machine for intrusion detection system. Pattern Recogn. 132, 108912 (2022)

    Article  Google Scholar 

  5. Alderete, N.A., Pathak, N., Espinosa, H.D.: Machine learning assisted design of shape-programmable 3d kirigami metamaterials. NPJ Comput. Mater. 8(1), 1–12 (2022)

    Article  Google Scholar 

  6. Ali, W., Ahmed, A.A.: Hybrid intelligent phishing website prediction using deep neural networks with genetic algorithm-based feature selection and weighting. IET Inf. Secur. 13(6), 659–669 (2019)

    Article  Google Scholar 

  7. Amiri, B., Gómez-Orellana, A.M., Gutiérrez, P.A., Dizène, R., Hervás-Martínez, C., Dahmani, K.: A novel approach for global solar irradiation forecasting on tilted plane using hybrid evolutionary neural networks. J. Clean. Prod. 287, 125577 (2021)

    Article  Google Scholar 

  8. Artrith, N., Urban, A., Ceder, G.: Constructing first-principles phase diagrams of amorphous li x si using machine-learning-assisted sampling with an evolutionary algorithm. J. Chem. Phys. 148(24), 241711 (2018)

    Article  Google Scholar 

  9. Attia, A.-F., Ismail, H.A., Selim, I.M., Osman, A.M., Isaa, I.A., Marie, M.A., Shaker, A.A.: Stellar population analysis of galaxies based on genetic algorithms. Chinese J. Astron. Astrophys. 5(4), 347 (2005)

    Article  Google Scholar 

  10. Azimi, Y., Khoshrou, S.H., Osanloo, M.: Prediction of blast induced ground vibration (bigv) of quarry mining using hybrid genetic algorithm optimized artificial neural network. Measurement 147, 106874 (2019)

    Article  Google Scholar 

  11. Bacardit, J., Stout, M., Hirst, J.D., Sastry, K., Llora, X., Krasnogor, N.: Automated alphabet reduction method with evolutionary algorithms for protein structure prediction. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 346–353 (2007)

    Google Scholar 

  12. Baes, M., Verstappen, J., De Looze, I., Fritz, J., Saftly, W., Pérez, E.V., Stalevski, M., Valcke, S.: Efficient three-dimensional nlte dust radiative transfer with skirt. Astrophys J. Suppl. Ser. 196(2), 22 (2011)

    Article  Google Scholar 

  13. Bagheri, S., Nan, W., Filizadeh, S.: Application of artificial intelligence and evolutionary algorithms in simulation-based optimal design of a piezoelectric energy harvester. Smart Mater. Struct. 29(10), 105004 (2020)

    Article  Google Scholar 

  14. Baier, A., Kerschbaum, F., Lebzelter, T.: Fitting of dust spectra with genetic algorithms-i. perspectives and limitations. Astron. Astrophys. 516, A45 (2010)

    Google Scholar 

  15. Baldominos, A., Saez, Y., Isasi, P.: On the automated, evolutionary design of neural networks: past, present, and future. Neural Comput. Appl. 32(2), 519–545 (2020)

    Article  Google Scholar 

  16. Balkaya, Ç., Ekinci, Y.L., Göktürkler, G., Turan, S.: 3d non-linear inversion of magnetic anomalies caused by prismatic bodies using differential evolution algorithm. J Appl. Geophys. 136, 372–386 (2017)

    Article  Google Scholar 

  17. Behler, J.: Four generations of high-dimensional neural network potentials. Chem. Rev. 121(16), 10037–10072 (2021)

    Article  Google Scholar 

  18. Bogdanos, C., Nesseris, S.: Genetic algorithms and supernovae type ia analysis. J. Cosmol. Astropart. Phys. 2009(05), 006 (2009)

    Article  Google Scholar 

  19. Brasil, C.R.S., Delbem, A.C.B., da Silva, F.L.B.: Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction. J. Comput. Chem. 34(20), 1719–1734 (2013)

    Article  Google Scholar 

  20. Cai, B., Pan, G.-L., Fu, F.: Prediction of the post-fire flexural capacity of rc beam using ga-bpnn machine learning. J Perform Const Facil (2020)

    Google Scholar 

  21. Cantó, J., Curiel, S., Martínez-Gómez, E.: A simple algorithm for optimization and model fitting: Aga (asexual genetic algorithm). Astron. Astrophys. 501(3), 1259–1268 (2009)

    Article  Google Scholar 

  22. Carr, B., Hart, W., Krasnogor, N., Hirst, J., Burke, E., Smith, J.: Alignment of protein structures with a memetic evolutionary algorithm. In: Proceedings of the 4th Annual Conference on Genetic and Evolutionary Computation, pp. 1027–1034 (2002)

    Google Scholar 

  23. Cassisi, S., Schlattl, H., Salaris, M., Weiss, A.: First full evolutionary computation of the helium flash-induced mixing in population II stars. Astrophys. J. 582(1), L43 (2002)

    Article  Google Scholar 

  24. Chan, C., Aleti, A., Heger, A., Smith-Miles, K.: Evolving stellar models to find the origins of our galaxy. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1129–1137 (2019)

    Google Scholar 

  25. Charbonneau, P.: Genetic algorithms in astronomy and astrophysics. Astrophys. J. Suppl. Ser. 101, 309 (1995)

    Article  Google Scholar 

  26. Chen, A., Choo, K., Astrakhantsev, N., Neupert, T.: Neural network evolution strategy for solving quantum sign structures. Phys. Rev. Res. 4(2), L022026 (2022)

    Article  Google Scholar 

  27. Chen, B., Wang, T., Li, C., Dai, H., Song, L.: Molecule optimization by explainable evolution. In: International Conference on Learning Representation (ICLR) (2021)

    Google Scholar 

  28. Chen, C., Ong, S.P.: A universal graph deep learning interatomic potential for the periodic table (2022). arXiv:2202.02450

  29. Chen, J., Chen, Y., Xianchen, X., Zhou, W., Huang, G.: A physics-guided machine learning for multifunctional wave control in active metabeams. Extrem. Mech. Lett. 55, 101827 (2022)

    Article  Google Scholar 

  30. Chen, W., Hasanipanah, M., Rad, H.N., Armaghani, D.J., Tahir, M.M.: A new design of evolutionary hybrid optimization of svr model in predicting the blast-induced ground vibration. Eng. Comput. 37

    Google Scholar 

  31. Cheng, G., Gong, X.-G., Yin, W.-J.: Crystal structure prediction by combining graph network and optimization algorithm. Nat. Commun. 13(1), 1–8 (2022)

    Google Scholar 

  32. Comin, A., Hartschuh, A.: Efficient optimization of shg hotspot switching in plasmonic nanoantennas using phase-shaped laser pulses controlled by neural networks. Opt. Exp. 26(26), 33678–33686 (2018)

    Article  Google Scholar 

  33. de França, F.O., Aldeia, G.S.I.: Interaction-transformation evolutionary algorithm for symbolic regression. Evol. Comput. 29(3), 367–390 (2021)

    Article  Google Scholar 

  34. De Geyter, G., Baes, M., Fritz, J., Camps, P.: Fitskirt: genetic algorithms to automatically fit dusty galaxies with a monte carlo radiative transfer code. Astron. Astrophys. 550, A74 (2013)

    Article  Google Scholar 

  35. Demetriou, D., See, L., Stillwell, J.: A spatial genetic algorithm for automating land partitioning. International Journal of Geographical Information Science 27(12), 2391–2409 (2013)

    Article  Google Scholar 

  36. Demetriou, D., Stillwell, J., See, L.M.: LandParcelS: A Module for Automated Land Partitioning. University of Leeds, School of Geography (2012)

    Google Scholar 

  37. Deringer, V.L., Caro, M.A., Csányi, G.: Machine learning interatomic potentials as emerging tools for materials science. Adv. Mater. 31(46), 1902765 (2019)

    Article  Google Scholar 

  38. Desai, S., Strachan, A.: Parsimonious neural networks learn interpretable physical laws. Sci. Rep. 11(1), 1–9 (2021)

    Article  Google Scholar 

  39. Durrant, J.D., Amaro, R.E., McCammon, J.A.: Autogrow: a novel algorithm for protein inhibitor design. Chem. Biol. Drug Des. 73(2), 168–178 (2009)

    Article  Google Scholar 

  40. Dushatskiy, A., Alderliesten, T., Bosman, P.A.N.: A novel surrogate-assisted evolutionary algorithm applied to partition-based ensemble learning. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 583–591 (2021)

    Google Scholar 

  41. Evans, B., Al-Sahaf, H., Xue, B., Zhang, M.: Evolutionary deep learning: a genetic programming approach to image classification. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp 1–6. IEEE (2018)

    Google Scholar 

  42. Faris, H., Ala’M, A.-Z., Heidari, A.A., Aljarah, I., Mafarja, M., Hassonah, M.A., Fujita, H.: An intelligent system for spam detection and identification of the most relevant features based on evolutionary random weight networks. Inf. Fus. 48, 67–83 (2019)

    Article  Google Scholar 

  43. Fishman, E.: Bikeshare: A review of recent literature. Trans. Rev. 36(1), 92–113 (2016)

    Article  Google Scholar 

  44. Gao, S., Song, S., Cheng, J., Todo, Y., Zhou, M.: Incorporation of solvent effect into multi-objective evolutionary algorithm for improved protein structure prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 15(4), 1365–1378 (2017)

    Article  Google Scholar 

  45. Gao, X., Lee, G.M.: Moment-based rental prediction for bicycle-sharing transportation systems using a hybrid genetic algorithm and machine learning. Comput. Ind. Eng. 128, 60–69 (2019)

    Article  Google Scholar 

  46. Genty, G., Salmela, L., Dudley, J.M., Brunner, D., Kokhanovskiy, A., Kobtsev, S., Turitsyn, S.K.: Machine learning and applications in ultrafast photonics. Nat. Photo. 15(2), 91–101 (2021)

    Article  Google Scholar 

  47. Ghimire, S., Deo, R.C., Downs, N.J., Raj, N.: Self-adaptive differential evolutionary extreme learning machines for long-term solar radiation prediction with remotely-sensed modis satellite and reanalysis atmospheric products in solar-rich cities. Remote Sens. Environ. 212, 176–198 (2018)

    Article  Google Scholar 

  48. Giuliani, M., Castelletti, A., Pianosi, F., Mason, E., Reed, P.M.: Curses, tradeoffs, and scalable management: Advancing evolutionary multiobjective direct policy search to improve water reservoir operations. J. Water Res. Plan. Manag. (2):04015050 (2016)

    Google Scholar 

  49. Guijo-Rubio, D., Durán-Rosal, A.M., Gutiérrez, P.A., Gómez-Orellana, A.M., Casanova-Mateo, C., Sanz-Justo, J., Salcedo-Sanz, S., Hervás-Martínez, C.: Evolutionary artificial neural networks for accurate solar radiation prediction. Energy 210, 118374 (2020)

    Article  Google Scholar 

  50. Guillén, A., Bueno, A., Carceller, J.M., Martínez-Velázquez, J.C., Rubio, G., Todero Peixoto, C.J., Sanchez-Lucas, P.: Deep learning techniques applied to the physics of extensive air showers. Astropart. Phys. 111, 12–22 (2019)

    Article  Google Scholar 

  51. Zahid Halim, Muhammad Nadeem Yousaf, Muhammad Waqas, Muhammad Sulaiman, Ghulam Abbas, Masroor Hussain, Iftekhar Ahmad, and Muhammad Hanif. An effective genetic algorithm-based feature selection method for intrusion detection systems. Computers & Security, 110:102448, 2021

    Google Scholar 

  52. Hegde, R.S.: Photonics inverse design: pairing deep neural networks with evolutionary algorithms. IEEE J. Sel. Top. Quantum Electron. 26(1), 1–8 (2019)

    Article  Google Scholar 

  53. Hosseini, S., Zade, B.M.H.: New hybrid method for attack detection using combination of evolutionary algorithms, SVM, and ANN. Comput. Netw. 173, 107168 (2020)

    Article  Google Scholar 

  54. Eduardo Raul Hruschka, Ricardo JGB Campello, Alex A Freitas, et al. A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 39(2):133–155, 2009

    Google Scholar 

  55. Ya-Lan, H., Chen, L.: A nonlinear hybrid wind speed forecasting model using lstm network, hysteretic elm and differential evolution algorithm. Energy Conver. Manag. 173, 123–142 (2018)

    Article  Google Scholar 

  56. Huang, J., Zhou, M., Zhang, J., Ren, J., Vatin, N.I., Sabri, M.M.S.: The use of ga and pso in evaluating the shear strength of steel fiber reinforced concrete beams. KSCE J. Civil Eng. 26(9), 3918–3931 (2022)

    Article  Google Scholar 

  57. Huang, Y., Dong, W., Chen, M., Zhang, K., Fang, D.: Evolutionary optimization design of honeycomb metastructure with effective mechanical resistance and broadband microwave absorption. Carbon 177, 79–89 (2021)

    Article  Google Scholar 

  58. Ivezić, Ž., Elitzur, M.: Self-similarity and scaling behaviour of infrared emission from radiatively heated dust-i. theory. Mon. Not. R. Astronom. Soc. 287(4), 799–811 (1997)

    Google Scholar 

  59. Joseph, T., Lazio, W., Cordes, J.M., Novak, J.: The genetic algorithm: searching for planets around pulsars. In: Bulletin of the American Astronomical Society, vol. 25, p. 1366 (1993)

    Google Scholar 

  60. Kang, S., Jeong, W., Hong, C., Hwang, S., Yoon, Y., Han, S.: Accelerated identification of equilibrium structures of multicomponent inorganic crystals using machine learning potentials. NPJ Comput. Mater. 8(1), 1–10 (2022)

    Article  Google Scholar 

  61. Khare, E., Yu, C.-H., Obeso, C.G., Milazzo, M., Kaplan, D.L., Buehler, M.J.: Discovering design principles of collagen molecular stability using a genetic algorithm, deep learning, and experimental validation. Proc. Nat. Acad. Sci. 119(40), e2209524119 (2022)

    Google Scholar 

  62. Kılıç, F., Yılmaz, İ.H., Kaya, Ö.: Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting. Renew. Energy 171, 176–190 (2021)

    Google Scholar 

  63. Kim, W., Baag, C.-E.: Rapid and accurate two-point ray tracing based on a quadratic equation of takeoff angle in layered media with constant or linearly varying velocity functions. Bull. Seismol. Soc. Am. 92(6), 2251–2263 (2002)

    Article  Google Scholar 

  64. Kim, W., Hahm, I.-K., Ahn, S.J., Lim, D.H.: Determining hypocentral parameters for local earthquakes in 1-d using a genetic algorithm. Geophys. J. Int. 166(2), 590–600 (2006)

    Google Scholar 

  65. Young Dae Ko: An efficient integration of the genetic algorithm and the reinforcement learning for optimal deployment of the wireless charging electric tram system. Comput. Ind. Eng. 128, 851–860 (2019)

    Article  Google Scholar 

  66. Kondo, T.: Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control. Appl. Soft Comput. 7(1), 189–202 (2007)

    Article  Google Scholar 

  67. Kowalski, M., Rubin, D., Aldering, G., Agostinho, R.J., Amadon, A., Amanullah, R., Balland, C., Barbary, K., Blanc, G., Challis, P.J. et al.: Improved cosmological constraints from new, old, and combined supernova data sets. Astrophys. J. 686(2), 749 (2008)

    Google Scholar 

  68. Kwon, Y., Kang, S., Choi, Y.-S., Kim, I.: Evolutionary design of molecules based on deep learning and a genetic algorithm. Sci. Rep. 11(1), 1–11 (2021)

    Article  Google Scholar 

  69. Le, T.C., Winkler, D.A.: Discovery and optimization of materials using evolutionary approaches. Chem. Rev. 116(10), 6107–6132 (2016)

    Google Scholar 

  70. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)

    Article  Google Scholar 

  71. Lei, Z., Gao, S., Zhang, Z., Zhou, M.C., Cheng, J.: Mo4: a many-objective evolutionary algorithm for protein structure prediction. IEEE Trans. Evol. Comput. 26(3), 417–430 (2021)

    Article  Google Scholar 

  72. Li, W., Zhao, D., He, C., Andong, H., Zhang, K.: Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations. Remote Sens. 12(5), 866 (2020)

    Article  Google Scholar 

  73. Xia Li and Anthony Gar-On Yeh: Integration of genetic algorithms and GIS for optimal location search. Int. J. Geograph. Inf. Sci. 19(5), 581–601 (2005)

    Article  Google Scholar 

  74. Li, Y., Ooi, H.K., Tchagang, A.: Deep evolutionary learning for molecular design (2020). arXiv:2102.01011

  75. Li, Y., Cheng, W., Yu, L.H., Rainer, R.: Genetic algorithm enhanced by machine learning in dynamic aperture optimization. Phys. Rev. Accel. Beams 21(5), 054601 (2018)

    Google Scholar 

  76. Wanzenböck, R., Arrigoni, M., Bichelmaier, S., Buchner, F., Carrete, J., Madsen, G.K.H.: Neural-network-backed evolutionary search for srtio3(110) surface reconstructions. Digit. Disc. 8 (2022)

    Google Scholar 

  77. Marzouq, M., Bounoua, Z., Fadili, H.E., Mechaqrane, A., Zenkouar, K., Lakhliai, Z.: New daily global solar irradiation estimation model based on automatic selection of input parameters using evolutionary artificial neural networks. J. Clean. Produ. 209, 1105–1118 (2019)

    Google Scholar 

  78. Mason, K., Duggan, J., Howley, E.: Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks. Energy 155, 705–720 (2018)

    Article  Google Scholar 

  79. Metcalfe, T.S.: Genetic-algorithm-based light-curve optimization applied to observations of the w ursae majoris star bh cassiopeiae. Astronom. J. 117(5), 2503 (1999)

    Google Scholar 

  80. Metcalfe, T.S., Charbonneau, P.: Stellar structure modeling using a parallel genetic algorithm for objective global optimization. J. Comput. Phys. 185(1), 176–193 (2003)

    Google Scholar 

  81. Moayedi, H., Moatamediyan, A., Nguyen, H., Bui, X.-N., Bui, D.T., Rashid, A.S.A.: Prediction of ultimate bearing capacity through various novel evolutionary and neural network models. Eng. Comput. 36(2), 671–687 (2020)

    Google Scholar 

  82. Mokiem, M.R., de Koter, A., Puls, J., Herrero, A., Najarro, F., Villamariz, M.R.: Spectral analysis of early-type stars using a genetic algorithm based fitting method. Astron. Astrophys. 441(2), 711–733 (2005)

    Google Scholar 

  83. Moster, B.P., Naab, T., Lindström, M., O’Leary, J.A.: Galaxynet: connecting galaxies and dark matter haloes with deep neural networks and reinforcement learning in large volumes. Mon. Not. R. Astronom. Soc. 507(2), 2115–2136 (2021)

    Google Scholar 

  84. Nanayakkara, V.K., Ikegami, Y., Uehara, H.: Evolutionary design of dynamic neural networks for evaporator control. Int. J. Ref. 25(6), 813–826 (2002)

    Google Scholar 

  85. Neshat, M., Nezhad, M.M., Abbasnejad, E., Mirjalili, S., Tjernberg, L.B., Garcia, D.A., Alexander, B., Wagner, M.: A deep learning-based evolutionary model for short-term wind speed forecasting: a case study of the lillgrund offshore wind farm. Energy Convers. Manag. 236, 114002 (2021)

    Google Scholar 

  86. Nesseris, S., Shafieloo, A.: A model-independent null test on the cosmological constant. Mon. Not. R. Astron. Soc. 408(3), 1879–1885 (2010)

    Article  Google Scholar 

  87. Nunkesser, R., Morell, O.: An evolutionary algorithm for robust regression. Comput. Stat. Data Anal. 54(12), 3242–3248 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  88. Owoyele, O., Pal, P., Torreira, A.V., Probst, D., Shaxted, M., Wilde, M., Senecal, P.K.: Application of an automated machine learning-genetic algorithm (automl-ga) coupled with computational fluid dynamics simulations for rapid engine design optimization. Int. J. Engine Res. 23(9), 1586–1601 (2022)

    Google Scholar 

  89. Paton, F.L., Maier, H.R., Dandy, G.C.: Including adaptation and mitigation responses to climate change in a multiobjective evolutionary algorithm framework for urban water supply systems incorporating ghg emissions. Water Res. Res. 50(8), 6285–6304 (2014)

    Article  Google Scholar 

  90. Pegg, S.C.-H., Haresco, J.J., Kuntz, I.D.: A genetic algorithm for structure-based de novo design. J. Comput.-Aided Mol. Des. 15(10), 911–933 (2001)

    Google Scholar 

  91. Podryabinkin, E.V., Tikhonov, E.V., Shapeev, A.V., Oganov, A.R.: Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning. Phys. Rev. B 99(6), 064114 (2019)

    Google Scholar 

  92. Puls, J., Urbaneja, M.A., Venero, R., Repolust, T., Springmann, U., Jokuthy, A., Mokiem, M.R.: Atmospheric NLTE-models for the spectroscopic analysis of blue stars with winds-II. line-blanketed models. Astron. Astrophys. 435(2), 669–698 (2005)

    Google Scholar 

  93. Ren, H., Fojtik, M.: Standard cell routing with reinforcement learning and genetic algorithm in advanced technology nodes. In: Proceedings of the 26th Asia and South Pacific Design Automation Conference, pp. 684–689 (2021)

    Google Scholar 

  94. Rolnick, D., Donti, P.L., Kaack, L.H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A.S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A. et al.: Tackling climate change with machine learning. ACM Comput. Surv. (CSUR) 55(2), 1–96 (2022)

    Google Scholar 

  95. Ruiz, A.N., Cora, S.A., Padilla, N.D., Domínguez, M.J., Vega-Martínez, C.A., Tecce, T.E., Orsi, Á., Yaryura, Y., Lambas, D.G., Gargiulo, I.D. et al.: Calibration of semi-analytic models of galaxy formation using particle swarm optimization. Astrophys. J. 801(2), 139 (2015)

    Google Scholar 

  96. Ruuzek, B., Kvasnicka, M.: Differential evolution algorithm in the earthquake hypocenter location. Pure Appl. Geophys. 158(4), 667–693 (2001)

    Article  Google Scholar 

  97. Safaei, M., Rezayan, H., Firouzabadi, P.Z., Sadidi, J.: Optimization of species distribution models using a genetic algorithm for simulating climate change effects on zagros forests in Iran. Ecol. Inf. 63, 101288 (2021)

    Google Scholar 

  98. Şahin, C.B., Dinler, Ö.B., Abualigah, L.: Prediction of software vulnerability based deep symbiotic genetic algorithms: phenotyping of dominant-features. Appl. Intell. 51(11), 8271–8287 (2021)

    Google Scholar 

  99. Sahni, V., Starobinsky, A.: Reconstructing dark energy. Int. J. Mod. Phys. D 15(12), 2105–2132 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  100. Salazar, J.Z., Reed, P.M., Herman, J.D., Giuliani, M., Castelletti, A.: A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control. Adv. Water Res. 92, 172–185 (2016)

    Google Scholar 

  101. Salcedo-Sanz, S., Cornejo-Bueno, L., Prieto, L., Paredes, D., García-Herrera, R.: Feature selection in machine learning prediction systems for renewable energy applications. Renew. Sust. Energy Rev. 90, 728–741 (2018)

    Article  Google Scholar 

  102. Sambridge, M., Gallagher, K.: Earthquake hypocenter location using genetic algorithms. Bull. Seismol. Soc. Am. 83(5), 1467–1491 (1993)

    Article  Google Scholar 

  103. Schlattl, H., Cassisi, S., Salaris, M., Weiss, A.: On the helium flash in low-mass population III red giant stars. Astrophys. J. 559(2), 1082 (2001)

    Article  Google Scholar 

  104. Seyedzadeh, S., Rahimian, Oliver, F.P.S., Rodriguez, S., Glesk, I.: Machine learning modelling for predicting non-domestic buildings energy performance: a model to support deep energy retrofit decision-making. Appl. Energy 279, 115908 (2020)

    Google Scholar 

  105. Sonnenberg, J. et al.: Fundamentals of land consolidation as an instrument to abolish fragmentation of agricultural holdings (2002)

    Google Scholar 

  106. Soto, D., Soto, W.: Evolutionary algorithm for solving supervised classification problems: an experimental study. In: 2022 6th International Conference on Intelligent Systems, Metaheuristics and Swarm Intelligence, pp. 24–29 (2022)

    Google Scholar 

  107. Y. Sun, Y. Jiao, C. Shi, Y. Zhang, Deep learning-based molecular dynamics simulation for structure-based drug design against sars-cov-2. Comput. Struct. Biotechnol. J. (2022)

    Google Scholar 

  108. Szustakowski, J.D., Weng, Z.: Protein structure alignment using a genetic algorithm. Prot.: Struct. Funct. Bioinf. 38(4), 428–440 (2000)

    Google Scholar 

  109. Tani, L., Rand, D., Veelken, C., Kadastik, M.: Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics. European Phys. J. C 81(2), 1–9 (2021)

    Article  Google Scholar 

  110. Theis, C.H., Kohle, S.: Multi-method-modeling of interacting galaxies-I. A unique scenario for NGC 4449? Astron. Astrophys. 370(2), 365–383 (2001)

    Google Scholar 

  111. Ünal, H.T., Başçiftçi, F.: Evolutionary design of neural network architectures: a review of three decades of research. Artif. Intell. Rev. 1–80 (2021)

    Google Scholar 

  112. Wahde, M.: Determination of orbital parameters of interacting galaxies using a genetic algorithm-description of the method and application to artificial data. Astron. Astrophys. Suppl. Ser. 132(3), 417–429 (1998)

    Article  Google Scholar 

  113. Wahde, M., Donner, K.J.: Determination of the orbital parameters of the m 51 system using a genetic algorithm. Astron Astrophys. 379(1), 115–124 (2001)

    Article  Google Scholar 

  114. Wall, M.: Galib: a c++ library of genetic algorithm components. Mech. Eng. Dep. MA Inst. Technol. 87, 54 (1996)

    Google Scholar 

  115. Wang, W., Tang, R., Li, C., Liu, P., Luo, L.: A BP neural network model optimized by mind evolutionary algorithm for predicting the ocean wave heights. Ocean Eng. 162, 98–107 (2018)

    Article  Google Scholar 

  116. Wang, Y., Chengru, W., Zhao, S., Wang, J., Bingfeng, Z., Han, M., Qing, D., Ni, M., Jiao, K.: Coupling deep learning and multi-objective genetic algorithms to achieve high performance and durability of direct internal reforming solid oxide fuel cell. Appl. Energy 315, 119046 (2022)

    Article  Google Scholar 

  117. Wierzbiński, M., Pławiak, P., Hammad, M., Acharya, U.R.: Development of accurate classification of heavenly bodies using novel machine learning techniques. Soft Comput. 25(10), 7213–7228 (2021)

    Google Scholar 

  118. Wu, Z., Kan, S.B.J., Lewis, R.D., Wittmann, B.J., Arnold, F.H.: Machine learning-assisted directed protein evolution with combinatorial libraries. Proc. Nat. Acad. Sci. 116(18), 8852–8858 (2019)

    Google Scholar 

  119. Hao, X., Chang, H., Zhang, D.: DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm. J. Comput. Phys. 418, 109584 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  120. Hao, X., Zhang, D.: Robust discovery of partial differential equations in complex situations. Phys. Rev. Res. 3(3), 033270 (2021)

    Article  Google Scholar 

  121. Hao, X., Zhang, D., Zeng, J.: Deep-learning of parametric partial differential equations from sparse and noisy data. Phys. Fluids 33(3), 037132 (2021)

    Article  Google Scholar 

  122. Haoran, X., Ma, J., Tan, P., Chen, B., Zhen, W., Zhang, Y., Wang, H., Xuan, J., Ni, M.: Towards online optimisation of solid oxide fuel cell performance: combining deep learning with multi-physics simulation. Energy AI 1, 100003 (2020)

    Article  Google Scholar 

  123. Yaseen, Z.M., Afan, H.A., Tran, M.-T.: Beam-column joint shear prediction using hybridized deep learning neural network with genetic algorithm. In: IOP Conference Series: Earth and Environmental Science, vol. 143, p. 012025. IOP Publishing (2018)

    Google Scholar 

  124. Yoon, E.J., Thorne, J.H., Park, C., Lee, D.K., Kim, K.S., Yoon, H., Seo, C., Lim, C.-H., Kim, H., Song, Y.-I.: Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk. Environ. Res. Lett. 14(2), 024001 (2019)

    Google Scholar 

  125. Yoshikawa, N., Terayama, K., Sumita, M., Homma, T., Oono, K., Tsuda, K.: Population-based de novo molecule generation, using grammatical evolution. Chem. Lett. 47(11), 1431–1434 (2018)

    Article  Google Scholar 

  126. Zhan, Z.-H., Li, J.-Y., Zhang, J.: Evolutionary deep learning: a survey. Neurocomputing 483, 42–58 (2022)

    Article  Google Scholar 

  127. Zhang, C.-X., Yuan, Y., Zhang, H.-W., Shuai, Y., Tan, H.-P.: Estimating stellar effective temperatures and detected angular parameters using stochastic particle swarm optimization. Res. Astron. Astrophys. 16(9), 008 (2016)

    Article  Google Scholar 

  128. Zhong, X., Velez, C., Acevedo, O.: Partial charges optimized by genetic algorithms for deep eutectic solvent simulations. J. Chem. Theory Comput. 17(5), 3078–3087 (2021)

    Article  Google Scholar 

  129. Zhou, Y., Zhou, N., Gong, L., Jiang, M.: Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine. Energy 204, 117894 (2020)

    Article  Google Scholar 

  130. Zuo, Y., Qin, M., Chen, C., Ye, W., Li, X., Luo, J., Ong, S.P.: Accelerating materials discovery with bayesian optimization and graph deep learning. Mater. Today 51, 126–135 (2021)

    Google Scholar 

Download references

Acknowledgements

The research reported in this work was supported in part by National Science Foundation under the grant 2110033, 1940099, and 1905775. The views, perspectives, and content do not necessarily represent the official views of the NSF. We appreciate the help of Dylan Johnson for proofreading the manuscript.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29201, USA

    Jianjun Hu, Yuqi Song, Sadman Sadeed Omee, Lai Wei, Rongzhi Dong & Siddharth Gianey

Authors
  1. Jianjun Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuqi Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sadman Sadeed Omee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lai Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rongzhi Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Siddharth Gianey
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianjun Hu .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

    Wolfgang Banzhaf

  2. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Penousal Machado

  3. School of Engineering and Computer Science and Centre for Data Science and Artificial Intelligence, Victoria University of Wellington, Wellington, New Zealand

    Mengjie Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hu, J., Song, Y., Omee, S.S., Wei, L., Dong, R., Gianey, S. (2024). Evolutionary Machine Learning in Science and Engineering. In: Banzhaf, W., Machado, P., Zhang, M. (eds) Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Springer, Singapore. https://doi.org/10.1007/978-981-99-3814-8_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-99-3814-8_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3813-1

  • Online ISBN: 978-981-99-3814-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation